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This work presents a multi-scale microstructural characterization of aluminum alloys processed by high-pressure torsion (HPT) and cold angular rolling process (CARP) to improve their mechanical properties. Mechanical properties such as microhardness and tensile strength were correlated with microstructural features. To understand the processing-structure-property relationships, characterization methods spanning nano- to millimeter scales were used, including X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), and scanning transmission electron microscopy (STEM) EDS. TEM and STEM EDS were used to show that HPT of a Mg sheet sandwiched between Al sheets successfully produced a supersaturated solid solution (SSSS) of Mg in Al and several Al-Mg intermetallic phases, leading to significant grain refinement and increases in microhardness over pure Al. Although CARP has potential to induce the severe plastic deformation (SPD), the CARP system used in this work was not able to achieve SPD aluminum alloys. However, SEM EBSD characterization shows that CARP achieves an increase of the low-angle grain boundaries (LAGBs) and geometrically necessary dislocation (GND) density in Al-1043,which improves the mechanical properties. Moreover, a preliminary study was conducted on CAPR processed Al-6061 alloys to understand the synergistic effects precipitation and CARP-processing on the microstructure and properties. This research provides the critical insights into the capabilities and current limitations of CARP as a continuous SPD technique for aluminum alloys, and demonstrate the importance of integrated multi-scale characterization in understanding advanced materials processing.
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Machine‐Learning‐Based, Feature‐Rich Prediction of Alumina Microstructure from Hardness
Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy.
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- Award ID(s):
- 2119833
- PAR ID:
- 10640401
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Discovery
- ISSN:
- 2943-9981
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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